Spatial advantages of highly educated individuals in Germany: Is sustainable mobility an expression of privilege?

To effectively combat climate change it is crucial to encourage daily environmentally friendly behaviour across large parts of the population. This includes daily mobility behaviour, since private transport is one of the largest contributors to greenhouse emissions. Previous studies suggest that highly educated individuals exhibit more environmentally friendly mobility behaviour, a fact that is usually explained by their higher environmental awareness. We instead explore the extent to which this behaviour is driven by their socio-spatial advantages. We use comprehensive data on daily mobility: our analytical sample includes 16,419 journeys from 4168 individuals in 2002 and 102,774 journeys from 26,036 individuals in 2017. The data is representative of German residents in large cities aged 18 to 59. We employ multilevel OLS regression, logistic regression, and fractional multinomial logit models to analyse changes in travel patterns among highly educated individuals over time. Our findings reveal that university graduates tend to reside not only more often in large cities but in the most central neighbourhoods within these areas, leading to shorter daily travel distances. Consequently, their daily journeys take less time and they are able to use slower, more sustainable mobility options when commuting, running errands, or engaging in leisure activities without incurring higher travel time costs than other groups. Our results highlight the importance of addressing residential inequalities as a key step in enabling a broader population to adopt sustainable lifestyles. • College educated individuals reside more often in large cities and within these in more central neighborhoods • Consequently, they travel shorter distances to reach points of interest (shopping, errands, etc.) compared to others • This advantage allows them to opt for sustainable but slower transport without incurring higher daily mobility time costs • The analysis utilizes daily mobility data from 100.000+ respondents representative of large German cities